Data, results and scripts for the article "Unveiling the sentiment behind central bank narratives: A novel deep learning index", published in the Journal of Behavioral and Experimental Finance (https://doi.org/10.1016/j.jbef.2023.100809). 

Change *.changetozip to *.zip and unzip the archive files.

The archive "1_Minutes_corpus_Czechia" includes 127 minutes for the Czech National Bank, starting from 25/01/2007 to 03/02/2022. 

The archive "2_Minutes_corpus_Hungary" contains 180 minutes for the Hungarian National Bank, starting from 22/01/2007 to 25/01/2022. Each document includes the corpus of both "Macroeconomic and financial market developments" and "The Council’s assessment and interest rate decision" documents. 

The archive "3_Minutes_corpus_Poland" contains 166 minutes for the National Bank of Poland, starting from 25/04/2007 to 04/01/2022. 

The archive "4_Minutes_corpus_Romania" contains 77 press releases on monetary policy decisions starting from to 09/02/2007 to 04/08/2016 and 41 minutes starting from 30/09/2016 to 09/02/2022 for the National Bank of Romania. 

All documents were hand collected from central banks' websites. The documents are labelled in the following format YYYY/MM/DD. The date corresponds to the publication of the document. 

The csv file "Monetary_policy_stance_labelling_all_central_banks" has 1,998 sentences corresponding to three monetary policy stances: hawkish, neutral, and dovish. The sentences have been manually annotated by the authors. 

The csv file "Central_bank_sentiment_indices" lists the sentiment indices obtained for the four central banks included in our sample. The sentiment indices are obtained by relying on Google’s Bidirectional Encoder Representations from Transformers (BERT). We fine-tuned the BERT model on our dataset of labeled sentences on central bank monetary policy stance. The fine-tuned BERT model was then used to predict the stance of central banks minutes and to construct a BERT central bank sentiment index (BERT-CBSI). Additionally, we fine-tune the financial domain specific FinBERT model by re-training it on our corpus of labeled sentences on central bank monetary policy stance (FinBERT-CBSI).

All the computations were run in Python. The scripts are made available, upon request, from authors. 

The authors,
14.04.2023
